MEDAL Report 2010001
NK Landscapes, Problem Difficulty, and Hybrid Evolutionary Algorithms
Martin Pelikan  (2010)

Abstract. This paper presents an empirical study of NK landscapes with the main focus on the relationship between (1) problem parameters, (2) various measures of problem difficulty of fitness landscapes, and (3) performance of hybrid evolutionary algorithms. As the target class of problems, the study considers two types of NK landscapes: (1) Standard, unrestricted NK landscapes and (2) shuffled NK landscapes with nearest-neighbor interactions. As problem difficulty measures, the paper considers the fitness distance correlation, the correlation coefficient, the distance of local and global optima, and the escape rate. Experimental results are presented, analyzed and discussed. Promising avenues for future work are also outlined.

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Missouri Estimation of Distribution Algorithms Laboratory
Department of Mathematics and Computer Science
University of Missouri-St. Louis, St. Louis, MO


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